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Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. This study proposes an efficient energy optimization in Smart Urban Buildings (SUBs) based on Improved Sine Cosine Algorithm (ISCA) that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a SUBs. The proposed system's goal is to optimize the energy of SUBs appliances in order to effectively regulate load demand, with the end result being a reduction in the peak to average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. The proposed system is evaluated by comparing it with the Grasshopper Optimization Algorithm (GOA) and unscheduled cases. Without applying an optimization algorithm, the total electricity cost, carbon emission, PAR and waiting time are equal to 1703.576 ID, 34.16664 (kW), and 413.5864s respectively for RTP. While, after applying GOA, the total electricity cost, carbon emission, PAR and waiting time are improved to 1469.72 ID, 21.17 (kW), and 355.772s respectively for RTP. While, after applying the ISCA Improves the total electricity cost, PAR, and waiting time by 1206.748 ID, 16.5648 (kW), and 268.525384s respectively. Where after applying GOA, the total electricity cost, PAR, and waiting time are improved to 13.72 %, 38.00 %, and 13.97 % respectively. And after applying proposed method, the total electricity cost, PAR, and waiting time are improved to 29.16 %, 51.51 %, and 35.07 % respectively. According to the results, the created ISCA algorithm performed better than the unscheduled case and GOA scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers. Furthermore, this study has presented a novel two-stage stochastic model based on Moth-Flame Optimization Algorithm (MFOA) for the co-optimization of energy scheduling and capacity planning for systems of energy storage that would be incorporated to grid connected smart urban buildings. • A energy transition AI model for sustainable electricity is designed. • The model is to schedule microgrid appliances and battery charging/discharging. • A new optimization technique is also introduced to reduce the cost of supplying the load. • Different AI techniques are also compared to find the more efficient one. • An optimized real time middle range grid connected energy systems is obtained. [ABSTRACT FROM AUTHOR]

The bang-bang relays of the multiple-boiler system (MBS) control, are characterized by complex limiter saturation functions and classified as fixed parameters. Their action signals cannot precisely control the nonlinear dynamic building heating demand over their entire range of operation. Moreover, in a mono-boiler system, the bang-bang controller endures increasing short cycling over partial load time due to the heating system being considered to have an oversized boiler at most times of running, thus promoting high energy consumption and fluctuating indoor thermal comfort. So, it is difficult to cope with uncertainties in outdoor environments and indoor heating load. Hence, this study formulates the MBS control problem as a dynamic Markov decision process and applies a deep clustering of reinforcement learning approach to obtain the optimal control policy through interaction with the environment based on multi-agent learning according to bang-bang action. With such an approach, adopting a new boiler sequencing control (BSC) strategy using deep clustering of reinforcement learning based on a bang-bang (DCRLBB) manner. The deep clustering is configured to break Lagrangian trajectory curves into piecewise segments to represent the RL agent's action policy. The agent's action policy signals are configured from the bang-bang reward formula based on trade-off implications to be more adjustable than traditional fixed parameters such as fuzzy bang-bang controller (FBBC). The agent of BSC significantly affects the energy performance of the MBS, whereas the other agent resizes boiler capacity by acting to adjust the boiler solenoid fuel valve. The comparison of results between the proposed strategy and conventional FBBC shows distinct differences in the superior response of DCRLBB under dynamic indoor/outdoor actual conditions and energy saving by more than 32% while maintaining the indoor thermal in the comfortable range. [Display omitted] • Proposed strategy based on DCRLBB is used to minimize short cycling on oversized boilers. • DCRLBB provides a viable approach to handling various cooperative policies of multi-agent • Clustering technique helps the well-fitting of the Lagrangian model to optimal agent policy. • Online learning for active decision-making enables the agent to predict the power demand of a multi-boiler. • The comparison of results for the DCRLBB shows energy saving by more than 32%. [ABSTRACT FROM AUTHOR]

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is suitable for multi-objective optimisation based on cooperative multi-agent systems (CMAS). The framework of DCLTML is used greedy iterative training to get an optimal set of weights and tabulated as a layer for each clustering structure. Such layers can deal with the challenges of large space and its massive data. Then the layer weights of each cluster are tuned by the Quasi-Newton (QN) algorithm to make the action sequence of CMAS optimal. Such a policy of CMAS effectively manipulates the inputs of the AHU, where the agents of the AHU activate the natural ventilation and set chillers into an idle state when the outdoor temperature crosses the recommended value. So, it is reasonable to assess the impact potential of thermal mass and hybrid ventilation strategy in reducing cooling energy; accordingly, the assigning results of the proposed DCLTML show that its main cooling coil saves >40% compared to the conventional benchmarks. Besides significant energy savings and improving environmental comfort, the DCLTML exhibits superior high-speed response and robustness performance and eliminates fatigue and wear due to shuttering valves. The results show that the DCLTML algorithm is a promising new approach for controlling HVAC systems. It is more robust to environmental variations than traditional controllers, and it can learn to control the HVAC system in a way that minimises energy consumption. The DCLTML algorithm is still under development, but it can potentially revolutionise how HVAC systems are controlled. [Display omitted] • Clustering based DCLTML structure is used to tackle an extremely large state-action space. • DCLTML provides a viable approach to handling high-dimensional dataset and massive data. • Clustering technique generated to represent a Lagrangian formula for agent action. • The quasi-Newton algorithm is a well-fitting of Lagrangian formula to best agent action. • The results for the DCLTML show saving >40% of main cooling coil energy. [ABSTRACT FROM AUTHOR]

Abstract The transfer of heat by the fins is influenced by the change of the direction of the fins. This paper investigates study the effect of the direction of longitudinal fins on a three-dimensional convection heat transfer in a rectangular channel and also study the effect of the lateral and longitudinal inclination of the rectangular channel. The Grashof range from 5 × 108 to 109, Reynolds from 1000 to 2300 and Prandtl 0.71. The bottom surface of the channel is exposed to constant heat flux, while other walls are isolated. Two cases are investigated. In case one, measurements were conducted for a lateral inclination of the channel, with a range of α = 0°,30°,60°, and 90°. Case two studied the longitudinal inclination of the channel, with the lateral inclination angle fixed at α = 90° and the longitudinal inclination angle Ө = 0°, 30°, 60°, and 70°. The dimensionless of fin height was Hf/H = 0.6, and the fin spacing was S/H = 0.17. The experimental results show that the coefficient of the heat transfer for lateral inclination (α = 0°) was greater than that for a sideways orientation (α = 90°). Additionally, the average coefficient of heat transfer for both the lateral and longitudinal inclination case is increased with the longitudinal inclination angle. The empirical equations are obtained based on the experimental results. These equations correlated the Nusselt number as a dependent variable of the orientations angles, Reynolds number, and the Grashof number, these equations are consistent with experimental results. Graphical abstract Image 1 Highlights • Investigate the effect of inclination and rotation angles of rectangular duct. • Experiments conducted on laminar mixed convection heat transfer from fins array. • Optimized a constant heat flux condition by tuning inclination and rotation angle. • A novel correlations for mixed convection heat transfer for various orientations. • The higher heat transfer coefficient is observed at the highest inclination angle. [ABSTRACT FROM AUTHOR]

The most distinctive properties of the HVAC systems are their large-scale nonlinear systems that contain large thermal inertia, time variability, nonlinear constraints, uncertain disturbance factors, multivariate systems and coupled properties for both temperature and humidity. This paper considers a novel control algorithm that could handle such intricate characteristics by using hybridization layers between the physical parameters' memory and the neural networks' weight, which is well-structured by the Takagi-Sugeno-Kang Fuzzy inference strategy. The application of nonlinear regression to the offline hybrid layers construction and online fine-tuning methods are conducted by using the Gauss-Newton Method in order to achieve fast tuning operation. The feedforward strategy is adopted, so as to boost the stability of the overall system in addition to increasing the control precision and its response speed. Moreover, the effects of disturbances and uncertainty are eradicated by online tuning. The tracking control goal takes full advantage of mature strategies regarding the predicted mean vote (PMV) to address high thermal inertia, to save energy and to tackle coupling problem. The proposed control performance results are analysed and compared to hybrid PID cascade control, where both strategies are tested individually and simultaneously through the use on the HVAC system. The obtained results showed that Feedforward Hybrid Layers Control (FHLC) led to effective advantages regarding optimal performance, adaptation, precision and robustness. Furthermore, adopted the adaptive structural control algorithm for FHLC to improve indoor thermal comfort, whereas the significant energy reduction is achieved. The prospective scope for future work is to expand the control structure for full building control by adding more controlled elements, such as lighting, ventilation, security, fire protection and other building appliances. [ABSTRACT FROM AUTHOR]

Despite past studies, no comprehensive models or empirical correlations cover all aspects of performances of cooling coils under different flow regimes (laminar, transition, and turbulent). Moreover, the cooling coil is characterized by a highly nonlinear dynamic subject to multiple inputs, coupling between the latent and sensible heat transfer modes, uncertain disturbances, and strong dependence of the overall heat transfer coefficient on the flow type, all causing significant challenges when it comes to modeling. Therefore, a hybrid layer structure model was adopted in this study to overcome these challenges. The new approach used two different optimization methods, Neural Networks' Weights and Takagi-Sugeno (TS) fuzzy, and the hybrid layers tuned by the Gauss-Newton algorithm (GNA). The proposed model covered three types of fluid flow to represent the dynamic behavior of the water-side and air-side heat transfer coefficients, each of which was divided into seven clusters and had its unique TS consequence. This study also administered meaningful fitness tests in the responses of the eleven independent variables that serve as its inputs. Furthermore, its application shows the control performance saving more than 44% of HVAC system energy. Based on the results, it was concluded that the proposed model is suitable for estimating energy and cost savings for electric power and water flow rate efficiency. In addition, the response of all types of output flow can be evaluated when changing eleven independent variables that are manipulated by three different controllers. [Display omitted] • Hybrid model structure systemized by novel fuzzy to cover all aspects of flow regime. • Layers mechanism is well suited for storing the parameters and weights of Takagi–Sugeno fuzzy. • The investigation for the right choice for the control performance saving more than 44% of buildings energy. • The eleven-dimensional structure are optimized using Gauss-Newton algorithm for nonlinear regression. • The outputs of eleven independent variables are systemized into a clustering method for all types of flow. [ABSTRACT FROM AUTHOR]

Integrated control by controlling both natural ventilation and HVAC systems based on human thermal comfort requirement can result in significant energy savings. The concept of this paper differs from conventional methods of energy saving in HVAC systems by integrating the control of both these HVAC systems and the available natural ventilation that is based on the temperature difference between the indoor and the outdoor air. This difference affects the rate of change of indoor air enthalpy or indoor air potential energy storage. However, this is not efficient enough as there are other factors affecting the rate of change of indoor air enthalpy that should be considered to achieve maximum energy saving. One way of improvement can be through the use of model guide for comparison (MGFC) that uses physical-empirical hybrid modelling to predict the rate of change of indoor air potential energy storage considering building fabric and its fixture. Three methods (normal, conventional and proposed) are tested on an identical residential building model using predicted mean vote (PMV) sensor as a criterion test for thermal comfort standard. The results indicate that the proposed method achieved significant energy savings compared with the other methods while still achieving thermal comfort. [ABSTRACT FROM AUTHOR]

In a hot and humid climate, HVAC (heating, ventilating and air conditioning) systems go through rigorous coupling procedures as a result of indoor conditions, which are significantly affected by the outdoor environment. Hence, a traditional method for addressing a coupling setback in HVAC systems is to add a reheating coil. However, this technique consumes a significant amount of energy. Three different strategies are designed in a hot and humid climate region, such as Basra, for AHUs (air handling unit), and their evaluations of decoupling are compared. The first and second strategies use the same feedback control references (temperature and relative humidity), except the second one also uses a reheating coil and a wet main cooling coil. The AHU (air handling unit) of the third (proposed) strategy is equipped with a dry main cooling coil and a wet pre-cooling coil to dehumidify fresh air, which allows the controller to handle the coupling problem. Furthermore, the proposed strategy utilises the PMV (predicted mean vote) index as a feedback control reference to increase optimisation parameters that provide more flexibility in meeting the thermal comfort sensation. The adaptive control algorithm of nonlinear multivariable systems is adopted to coordinate these three policies of optimisation. The results of the three strategies show that the proposed scheme achieved the desired thermal comfort, superior performance, adaptation, robustness and implementation without using a reheating coil. [ABSTRACT FROM AUTHOR]

[Display omitted] • Clustering based hybrid network structure is used to tackle an extremely large state-action space. • Converting the TS inference into hybrid layers enables HDCMARL to deal with the continuous actions space. • Clustering structure generated by novel TSF rules for systemizing multi-agent policy. • Quasi-Newton algorithm is well tuning the parameters and weights of policy for storing at hybrid layers. • The investigation for the HDCMARL performance saving more than 32% of HVAC energy. The heating, ventilating and air conditioning (HVAC) systems energy demand can be reduced by manipulating indoor conditions within the comfort range, which relates to control performance and, simultaneously, achieves peak load shifting toward off-peak hours. Reinforcement learning (RL) is considered a promising technique to solve this problem without an analytical approach, but it has been unable to overcome the awkwardness of an extremely large action space in the real world; it would be quite hard to converge to a set point. The core of the problem with RL is its state space and action space of multi-agent action for building and HVAC systems that have an extremely large amount of training data sets. This makes it difficult to create weights layers accurately of the black-box model. Despite the efforts of past works carried out on deep RL, there are still drawback issues that have not been dealt with as part of the basic elements of large action space and the large-scale nonlinearity due to high thermal inertia. The hybrid deep clustering of multi-agent reinforcement learning (HDCMARL) has the ability to overcome these challenges since the hybrid deep clustering approach has a higher capacity for learning the representation of large space and massive data. The framework of RL agents is a greedy iterative trained and organized as a hybrid layer clustering structure to be able to deal with a non-convex, non-linear and non-separable objective function. The parameters of the hybrid layer are optimized by using the Quasi-Newton (QN) algorithm for fast response signals of agents. That is to say, the main motivation is that the state and action space of multi-agent actions for building HVAC controls are exploding, and the proposed method can overcome this challenge and achieve 32% better performance in energy savings and 21% better performance in thermal comfort than PID. [ABSTRACT FROM AUTHOR]

Abstract: Controllers of HVAC systems are expected to be able to manipulate the inherent nonlinear characteristics of these large scale systems that also have pure lag times, big thermal inertia, uncertain disturbance factors and constraints. In addition, indoor thermal comfort is affected by both temperature and humidity, which are coupled properties. To control these coupled characteristics and tackle nonlinearities effectively, this paper proposes an online tuned Takagi–Sugeno Fuzzy Forward (TSFF) control strategy. The TS model is first trained offline using Gauss–Newton Method for Nonlinear Regression (GNMNR) algorithm with data collected from both building and HVAC system equipments. The model is then tuned online using the gradient algorithm to enhance the stability of the overall system and reject disturbances and uncertainty effects. As control objective, predicted mean vote (PMV) is adopted to avoid temperature–humidity coupling, thermal sensitivity and to save energy at the same time. The proposed TSFF control method is tested in simulation taking into account practical variations such as thermal parameters of buildings, weather conditions and other indoor residential loads. For comparison purposes, normal Takagi–Sugeno fuzzy and hybrid PID Cascade control schemes were also tested. The results demonstrated superior performance, adaptation and robustness of the proposed TSFF control strategy. [Copyright &y& Elsevier]

Abstract: This work presents a hybrid model to be used for effectively controlling indoor thermal comfort in a heating, ventilating and air conditioning (HVAC) system. The first modeling part is related to the building structure and its fixture. Since building models contain many nonlinearities and have large thermal inertia and high delay time, empirical calculations based on the residential load factor (RLF) is adopted to represent the model. The second part is associated with the indoor thermal comfort itself. To evaluate indoor thermal comfort situations, predicted mean vote (PMV) and predicted percentage of dissatisfaction (PPD) indicators were used. This modeling part is represented as a fuzzy PMV/PPD model which is regarded as a white-box model. This modeling is achieved using a Takagi-Sugeno (TS) fuzzy model and tuned by Gauss-Newton method for nonlinear regression (GNMNR) algorithm. The main reason for combining the two models is to obtain a proper reference signal for the HVAC system. Unlike the widely used temperature reference signal, the proposed reference signal resulting from this work is closely related to thermal sensation comfort; Temperature is one of the factors affecting the thermal comfort but is not the main measure, and therefore, it is insignificant to control thermal comfort when the temperature is used as the reference for the HVAC system. The overall proposed model is tested on a wide range of parameter variation. The corresponding results show that a good modeling capability is achieved without employing any complicated optimization procedures for structure identification with the TS model. [Copyright &y& Elsevier]

• A novel integrated two different types to control a large-scale nonlinear system. • Converting Takagi-Sugeno fuzzy inference system into faster hybrid layers structure. • Spans of the fuzzy membership functions are tuned on-line to achieved energy saving. • Online tuning by downhill simplex algorithm achieved minimum time response. • Integrated control deal with plants possess lag time, big inertia and uncertainty. In some fields, such as the semiconductor manufacturing process, museum, pharmaceutical, and medicine manufacturing industry, the HVAC system needs a very fast response time to protect products and more energy-efficient buildings than traditional controllers. So, the proposed controller is designed to overcome such problems by using integrated fuzzy PI-PD Mamdani-type (FPIPDM) and cluster adaptive training based on Takagi-Sugeno-Kang (CABTSK) type. The spans of the fuzzy membership functions of the FPIPDM are tuned online by the Nelder-Mead simplex search (NMSS) algorithm to minimize time response, while the CABTSK model is tuned offline and online using a gradient descent (GD) algorithm to enhance the stability of the overall system and reject disturbances. Then, the integration framework is used to enable the concept of time-optimal based on the bang-bang code delegation. In this sense, a selected switch delegates the execution of proper control code to the action processor that provides computational resources to control indoor conditions. The predicted mean vote (PMV) index provides a higher comfort level than the temperature, as it considers six variables related to thermal comfort. The results of the proposed structure show that it improves the overall output accuracy and significantly reduces the response time. Furthermore, it increases the robustness of the indoor conditions and it is quite applicable to the MIMO HVAC systems processes with strong coupling actions between temperature and humidity, large time delay, noise, disturbances, nonlinearities, and imprecise identification model. [ABSTRACT FROM AUTHOR]

• Hybrid network structure fabricated by novel TS-FS for forecast HVAC Energy Demand. • CMM structure is well suited for storing the parameters and weights of TS-FS. • The investigation for the right choice for the zone location saving more than 50 % of HVAC system energy. • Thermal comfort zone on the psychrometric chart of ASHRAE Standard is used to select the optimal zone. • The input-output data set are systemized into a novel clustering method which to improve forecasting performance. the HVAC systems consume more than half of the total buildings energy demand, forecasting the cooling/heating load of the building is important to predict buildings energy demand. The energy assessment tools such as a model for forecasting building energy consumption is based on outdoor thermal conditions, the outdoor conditions are highly nonlinear in real life cannot be represented by linear differential equations and have an uncertain disturbance nature. This paper contrives a novel nonlinear model structure to cope with such difficulty, which is composed of two hybrid nonlinear forms, Takagi-Sugeno fuzzy system (TS-FS) and Neural Networks' Weights. Such a structure has many advantages, including suitability for multi-layer implementations like an integrated eight-dimension net of parameters and weights which represents model input-output relations of a nonlinear system. The Gauss-Newton algorithm is used to tune model weights and parameters for the fitting of nonlinear regression of clusters model to data. The main feature of the proposed model is to express the dynamic conditions of the outdoor thermal environment of each fuzzy implication by a cluster functions model and thus promote the prediction performance. The overall proposed model is tested on the training and validation of multizone then compared with the RLF model. The corresponding results show that a better hybrid modelling and uncertainty mitigation which is achieved without significant loss of prediction accuracy. [ABSTRACT FROM AUTHOR]

Exergy analysis is essential for evaluating the second law of thermodynamics efficiency in solar thermal applications such as parabolic trough collectors (PTCs). This study creates ML models to tackle complex challenges in renewable energy systems and components. Six prediction models were developed such as Adaptive Boosting (AdaBoost), Multivariate adaptive regression splines (MARS), Stochastic Gradient Descent (SGD), Tweedie Regressor, voting, and stacking ensemble learning, were developed to predict the exergy efficiency of PTCs. The base fluids were three molten salts (Solar Salt, Hitec, and Hitec XL). Three nanoparticle types (Al 2 O 3 , CuO, and SiO 2) were mixed homogeneously in a single-phase approach to prepare nine nanofluids. The output was predicted based on different input parameters such as molten salts, nanoparticle types, input temperature, volume fraction, Reynolds number (Re), Nusselt number (Nu), and friction factor (f). The results indicated that the stacking regressor efficiently predicted the exergy efficiency using (3-5) input parameters with a coefficient of determination (R2 = 0.963), followed by the AdaBoost algorithm with R2 = 0.947 using the fifth input combination over the testing phase. Further, AdaBoost and Stacking Regressors models were compared with the previously published study and showed an overall accuracy of R2 = 0.9472 and R2 = 0.9634, respectively. [ABSTRACT FROM AUTHOR]

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies. [Display omitted] • New hybrid machine learning (ML) models were developed for heavy metals prediction. • As (mg/kg) and zinc Zn (mg/kg) in marine sediments Algeciras Bay were predicted. • Akaike and Schwarz information criteria were used for data stationarity appraisal. • The proposed hybrid ML model exhibited the superior prediction performance. • The study provides insights for environmental decision-makers for proper management. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) algorithms can potentially contribute to optimizing energy and exergy outputs in renewable resources to increase efficiencies and reduce environmental risk. This study utilized tree-based, linear, and non-linear regression techniques to predict the energy and exergy efficiency of Parabolic Trough Solar Collectors (PTSCs) using oil-based nanofluids. The cooling fluids were prepared from three main oil types, namely Therminol VP-1, Syltherm 800, and Dowtherm Q mixed with three metallic oxides, including Al 2 O 3 , CuO, and SiO 2 , in various volume fractions. The two outputs were predicted according to a range of input parameters, namely Volume Fraction (%), Reynolds Number (Re), Inlet Fluid Temperature, Direct Solar Irradiance, Nusselt Number (Nu), and Friction Factor (f). Ensemble approaches such as Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR), Classification and Regression Trees (CART), and Adaptive Boosting (AdaBoost) were the top-performing models in the model selection process out of nine. The modeling results showed that, CART was the top model in predicting the energy efficiency using Syltherm 800-SiO 2 nanofluid with R2 = 0.9999. Meanwhile, ETR was the top model in predicting the exergy efficiency using Dowtherm Q-SiO 2 nanofluid with R2 = 0.9988. Moreover, in the business insights, the maximum errors in the energy and exergy models were observed (1.43 % and 1.97 %) using Therminol VP-1, (1.3 % and 2.44 %) using Syltherm 800 and Syltherm 800-CuO and (1.15 % and 2 %) using Dowtherm Q and Dowtherm Q-CuO, respectively. [Display omitted] • AI algorithms were used for precise prediction of PTSCs efficiencies. • Different oil-based nanofluid with different oils and metallic oxides were tested. • Ensemble models outperformed in predicting energy and exergy efficiencies. • Best AI models attained for (Syltherm 800-SiO 2 -energy and Dowtherm Q-SiO 2 -exergy). • Error results range (1.15–2.44 %) underscore the reliability of models establishment. [ABSTRACT FROM AUTHOR]

• The impact of adding low-cost energy storage materials (salt balls and sponges) on the performance of single-slope solar still is being evaluated. • The low-cost energy storage materials enhanced the heating of the saltwater and decreased the cost of freshwater production. • The productivity for all tested solar stills decreased with the increase in water depth. In the current research, a low-cost energy storage material was utilized to improve the performance of single-slope solar distillers. To this end, a conventional distiller was modified with low-cost energy storage materials by adding twenty-five spherical salt balls and seventeen sponges to the bottom of the basin at different water depths, and its performance was examined under the climate of Al- Hilla, Iraq. Therefore, two cases were tested; 25 salt balls + 17 sponges in 1.5 cm depth of water (Case 1) and 25 salt balls + 17 sponges in 2 cm depth of water (Case 2), and their results were compared with the conventional solar distiller. It was found that using reasonable energy storage materials (salt balls and sponges) has a good role in increasing the productivity of modified solar distillers. Also, the daily accumulated productivity of modified solar still (MSS) for Case 1 was higher than that for Case 2 and CSS by about 16.86% and 44.32%, respectively. The maximum productivity was attained for Case 1, which was 1934 g/m2, 1655 g/m2, and 1340 g/m2 for both modified solar still (MSS-SBS) for Case 2 and CSS, respectively. Finally, it is recommended to use low-cost energy storage materials such as salt balls and sponges with a minimum depth of basin water to improve the productivity of the solar still. [ABSTRACT FROM AUTHOR]

• Different studies of greenhouse-earth air heat exchanger systems are reviewed. • The effects of geometrical parameters, configurations, and operation conditions are presented. • Using an earth air heat exchanger can effectively cover the greenhouse thermal loads. • Integrating the photovoltaic and photovoltaic/thermal modules can improve the performance of the SG-EAHE systems. In recent years, yearly climatic changes, continuous temperature increases, and the impact of global environmental change have seriously affected agricultural production. The solar greenhouse (SG) system is designed to maintain suitable temperatures and humidity levels for cultivating plants. For this purpose, an earth-to-air heat exchanger (EAHE) can be coupled with the SG to provide the necessary heating and cooling required to maintain suitable conditions for vegetation. This review presents a comprehensive literature survey on SG-EAHE systems. The thermal characteristics of heating and cooling modes are presented for SG-EAHE systems. Reports indicate that integrating EAHE with the SG can meet the heating and cooling needs of the SG while significantly reducing water consumption. The design parameters of EAHE, such as configuration, pipe diameter, pipe length, and buried depth, can affect the performance of SG-EAHE systems. Additionally, integrating photovoltaic (PV) and photovoltaic/thermal (PVT) systems with SG-EAHE systems was discussed. Moreover, the challenges and prospective aspects of SG-EAHE systems were identified. [ABSTRACT FROM AUTHOR]

Dispersion-Shifted Fiber (DSF) is essential for reducing chromatic dispersion in high-speed optical communication systems. This study investigates the influence of Four Wave Mixing (FWM) on the quality of signals in orthogonal channels. We examine the advantages of DSF technology and analyze the impact of modulation formats such as On-Off Keying with Return-to-Zero (OOK-RZ) and Duo Binary Modulation class-1 (DBM-1) on transmission performance at different distances. This research assesses the efficacy of orthogonal channels in mitigating four-wave mixing (FWM) effects and improving the overall performance of eight-channel systems at distances of 100 km and 200 km through computer simulations. The results of our study show notable enhancements, namely in optimizing the Q-factor (a metric for signal quality) and reducing bit error rates when employing orthogonal channels compared to previous work. By integrating orthogonal channels with OOK-RZ modulation, we achieved higher performance and reduced nonlinear impairments in a simulated eight-channel system with 50 GHz spacing and 80 Gb/s data rates. This effect was particularly pronounced at high input power levels. At an input power of 20 dBm and a distance of 200 km, this particular combination yielded a maximum Q-factor of 27.25 and a minimum FWM power of −54 dBm. In comparison, under the same conditions, the use of OOK-RZ alone resulted in an FWM power of −24 dBm and a Q-factor of only 1.63. This research provides vital insights into enhancing the efficiency and dependability of optical communication systems, hence facilitating breakthroughs in high-speed data transfer and network scalability. • FWM crosstalk is greatly influenced by modulation quality, channel orthogonality, and dispersion conditions. • A new approach involving the combined utilization of orthogonal dense channels with OOK-RZ modulation has demonstrated superior system performance and minimized nonlinear defects, • New technique showcases improved efficiency and reliability in data transmission, offering potential advancements in various applications where signal integrity and robustness are paramount. • A technique for suppressing Four-Wave Mixing (FWM) crosstalk has been successfully implemented at dispersion values close to zero, with high pumping power, and dense channel spacing. • Interference is reduced under extreme conditions such as dense channel spacing, high pump power and low dispersion condition. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These models facilitate proactive measures such as early warning systems (EWSs), evacuation planning, and resource allocation, addressing the substantial challenges associated with natural disasters. This study offers a comprehensive exploration of trustworthy AI applications in natural disasters, encompassing disaster management, risk assessment, and disaster prediction. This research is underpinned by an extensive review of reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), and Web of Science (WoS). Three queries were formulated to retrieve 981 papers from the earliest documented scientific production until February 2024. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, 108 studies were included in the quantitative synthesis. This study provides a specific taxonomy of AI applications in natural disasters and explores the motivations, challenges, recommendations, and limitations of recent advancements. It also offers an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution addresses seven open issues and provides critical solutions through essential insights, laying the groundwork for various future works in trustworthiness AI-based natural disaster management. Despite the potential benefits, challenges persist in the application of AI to natural disaster management. In these contexts, this study identifies several unused and used areas in natural disaster-based AI theory, collects the disaster datasets, ML, and DL techniques, and offers a valuable XAI approach to unravel the complex relationships and dynamics involved and the utilization of data fusion techniques in decision-making processes related to natural disasters. Finally, the study extensively analyzed ethical considerations, bias, and consequences in natural disaster-based AI. [ABSTRACT FROM AUTHOR]

Predictions of thermal performance (η) of flat plate solar collectors (FPSCs) can provide essential information for diverse engineering applications such as thermal and energy areas. Several thermal and operating parameters influence η, and its prediction and quantification are highly complex and challenging. The current research was adopted to investigate the potential of different machine learning (ML) models, including Hist Gradient Boosting (HGBR), Multivariate Adaptive Regression Splines (MARS), Tweedie Regressor (TR), and Stacking Regressor (SR) for η of FPSC prediction using Al 2 O 3 –H 2 O nanofluids. The prediction matrix was established using five predictors, i.e., nanoparticle size, the collector slope, the absorbed energy parameter, the removed energy parameter, and the reduced temperature parameter. Different input combinations were constructed based on Forward Feature Selection (FFS) integrated with the Random Forest (RF) Algorithm. Results indicated that Model 5 performed the best in terms of all the metrics considered, with the lowest Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), U 95 (95th Percentile Uncertainty) and residual error, and the highest Coefficient of Determination (R2), Pearson correlation coefficient (PCC), Nash-Sutcliffe Coefficient (NSE), and Willmott's Index (WI). Model 4 also performed well, especially in terms of R2 and PCC. Models 2 and 3 performed reasonably well, while Model 1 had the lowest performance across most metrics. According to model-5, the stacking algorithm was more efficient than HGBR, MARS, and Tweedie, with prediction accuracy of 94.5%, 92.9%, 92.7%, and 89.9%, respectively. Overall, the research offered insightful results on η prediction for several thermal predictors. • Thermal efficiency of FPSCs is predicted using advanced machine learning models. • Five predictors were used to develop the prediction matrix. • Forward Feature Selection approach is applied for optimal feature selection inputs. • Stacking algorithm provides accurate prediction for η with MAEP = 20.7%. • The study provided insightful predictions for several thermal predictors. [ABSTRACT FROM AUTHOR]

Improving the thermo-physical characteristics of water by the simple process of suspending nano-size particles may enhance the performance of solar distillation systems. More solar energy can be absorbed by the nanofluid and condensing cover of the solar still, thanks to the enhanced characteristics that magnify the temperature differential between the two. To systematically evaluate the latest progress in using nanofluids in a solar still energy system, this review intends to cover the most recent published studies between 2020 and 2024.Examining the impact of integrated hybrid nanofluid with solar still energy systemson water productivity is the primary focus of this review. The analysis also highlights different key aspects, including the system layout, type and concentration of nanoparticles, as well as water productivity. The findings demonstrate that by adding Al 2 O 3 at concentrations of0.1 %,0.2 %, and 0.3 %, traditional solar still productivity can be enhanced to 4.9, 5.47, and 6.12 L/m2, respectively. In addition, as compared to a standard solar still devoid of nanoparticles, the cumulative productivity of a modified solar still using a hybrid nanofluid may be elevated by 11.6 %. Additionally, hybrid nanofluids may increase daily water production by 27.2 % in the summer and 21.7 % in the winter, all because of their differing operating temperatures. To further promote the use of nanofluids in solar still energy systems and guarantee an increase in total efficiency, this investigation provides a number of research recommendations for future studies. Seemingly, this has the potential to increase the utilisation of hybrid nanofluids in solar distillationsystems with the aim of boosting the absorbed energy. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) have attracted considerable interest recently due to their remarkable thermal energy storage capabilities for different thermal applications. This would effectively assist in the reduction of CO 2 emissions and encourage the transition to sustainable energy on a global scale. The present study investigates the increase in the thermal efficiency of a shell-and-tube heat exchanger using PCM and a novel arrangement of fins in the form of a tree. The enthalpy-porosity method is used to mimic phase changes under the studied conditions. Moreover, copper nanomaterials are incorporated with the PCM to increase thermal conductivity and speed up the charging process. The influences of various parameters, such as the concentration of nano-additive and the distribution of sub-fins, are investigated. The findings derived from the present model demonstrate that the incorporation of elongated sub-fins in the outer part (far from the inner heating tube) of the PCM unit leads to a noteworthy enhancement in the heat transfer distribution, resulting in a 75.7% reduction in melting time. Adding copper nanomaterials to PCM improves thermal performance. For example, a 6% concentration of nanoparticles leads to a reduction in the melting process by >15% compared to pure PCM. [ABSTRACT FROM AUTHOR]